Modern motor vehicles are being provided to an increasing extent with the capacity for autonomous driving. The driving of a motor vehicle on its own presupposes that information about the environment of the motor vehicle will be made available. For instance, a motor vehicle designed for autonomous driving has to be able to recognize other road users such as secondary vehicles and pedestrians, for example.
A further important aspect relates to the roadway surface currently being driven on by a motor vehicle in the given case. The driver of a motor vehicle typically registers the condition of the roadway visually, adapting his/her driving style appropriately. If, for example, the roadway appears to the driver to be slippery or smooth, the driver typically drives more cautiously and, where appropriate, also activates suitable systems such as an all-wheel drive, for example.
On the other hand, in the case of a motor vehicle driving on its own or, to be more exact, autonomously, it is necessary that the motor vehicle itself inspects the roadway surface. An automated estimation of the friction of the roadway can contribute toward making the control of the vehicle more intelligent, particularly in the case of the autonomous driving of a motor vehicle.
Diverse methods are known in the state of the art that enable differing types of roadway to be differentiated by utilizing camera systems. One technology that is employed in this connection involves the connection of two cameras, each equipped with a polarizing filter, said polarizing filters having been rotated by an angle of 90° in relation to one another. Since the light polarized on a wet surface is registered only by the camera having the appropriate polarizing filter, from the difference between the respective camera images it can be inferred, for example, whether the vehicle is driving on a wet roadway. However, this method cannot be used in a vehicle that is equipped with only one camera.
In another approach, differing image parameters—such as, for example, luminescence, color information and surface structure—are extracted, and the corresponding properties are used for the purpose of training a classifier. However, known approaches are correspondingly limited to the use of color cameras and also to the use of a limited number of properties or filters.
With respect to the state of the art, reference is made merely in exemplary manner to WO 2013/173911 A1,US 2013/0226421 A1, CN 101609607 A, JP 2002162343 A and JP 2002162343 A.